envdrift init¶

Generate a Pydantic Settings class from an existing .env file.

Synopsis¶

envdrift init [ENV_FILE] [OPTIONS]

Description¶

The init command bootstraps your schema by generating a Pydantic Settings class from an existing .env file. It:

Infers types - Detects booleans, integers, and strings
Detects sensitive fields - Marks likely secrets with sensitive=True
Creates required fields - String values become required (no default)
Adds defaults - Boolean and integer values get defaults

This is useful when:

Starting with an existing project that has .env files
Migrating from dotenv to pydantic-settings
Creating a baseline schema to customize

Arguments¶

Argument	Description	Default
`ENV_FILE`	Path to the .env file to read	`.env`

Options¶

`--output`, `-o`¶

Output file path for the generated Settings class.

envdrift init .env --output config/settings.py
envdrift init .env -o settings.py

`--class-name`, `-c`¶

Name for the generated Settings class.

envdrift init .env --class-name AppConfig
envdrift init .env -c ProductionSettings

`--detect-sensitive` / `--no-detect-sensitive`¶

Control automatic detection of sensitive variables.

# Enable detection (default)
envdrift init .env --detect-sensitive

# Disable detection
envdrift init .env --no-detect-sensitive

Examples¶

Basic Generation¶

envdrift init .env

Input .env:

DATABASE_URL=postgres://localhost/mydb
API_KEY=sk-123456
DEBUG=true
PORT=8000
LOG_LEVEL=INFO

Generated settings.py:

"""Auto-generated Pydantic Settings class."""

from pydantic import Field
from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    """Settings generated from .env."""

    model_config = SettingsConfigDict(
        env_file=".env",
        extra="forbid",
    )

    API_KEY: str = Field(json_schema_extra={"sensitive": True})
    DATABASE_URL: str = Field(json_schema_extra={"sensitive": True})
    DEBUG: bool = True
    LOG_LEVEL: str
    PORT: int = 8000

Custom Output Path¶

envdrift init .env.production --output config/production.py

Custom Class Name¶

envdrift init .env --output settings.py --class-name AppSettings

Without Sensitive Detection¶

envdrift init .env --no-detect-sensitive

All fields will be generated without sensitive=True, even if they look like secrets.

Type Inference¶

.env Value	Inferred Type	Has Default
`true`, `false`	`bool`	Yes
`123`, `8000`	`int`	Yes
`hello`, `postgres://...`	`str`	No (required)

Sensitive Detection¶

Variables are marked sensitive if their name matches:

*_KEY, *_SECRET, *_TOKEN
*_PASSWORD, *_PASS
*_CREDENTIAL*, *_API_KEY
JWT_*, AUTH_*, *_DSN

Or if their value matches patterns:

API keys: sk-*, pk-*
AWS keys: AKIA*
Database URLs with credentials
JWT tokens

Generated Schema Features¶

The generated schema includes:

model_config = SettingsConfigDict(
    env_file=".env",      # Reads from .env by default
    extra="forbid",       # Rejects unknown variables
)

This provides strict validation out of the box.

Customization¶

After generating, you should customize:

Add descriptions:

DATABASE_URL: str = Field(
    description="PostgreSQL connection string",
    json_schema_extra={"sensitive": True}
)

Add validators:

@field_validator("PORT")
@classmethod
def validate_port(cls, v: int) -> int:
    if not 1 <= v <= 65535:
        raise ValueError("Invalid port number")
    return v

Split by environment:

class BaseSettings(BaseSettings):
    DATABASE_URL: str = Field(json_schema_extra={"sensitive": True})

class DevelopmentSettings(BaseSettings):
    DEBUG: bool = True

class ProductionSettings(BaseSettings):
    DEBUG: bool = False

Workflow¶

Generate initial schema:

envdrift init .env --output config/settings.py

Review and customize:
Add descriptions
Adjust types if needed
Add validators
Mark additional sensitive fields
Validate:

envdrift validate .env --schema config.settings:Settings

Set up pre-commit:

envdrift hook --config